English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The use of deep neural networks (DNNs) in safety-critical applications likemobile health and autonomous driving is challenging due to numerousmodel-inherent shortcomings. These shortcomings are diverse and range from alack of generalization over insufficient interpretability to problems withmalicious inputs. Cyber-physical systems employing DNNs are therefore likely tosuffer from safety concerns. In recent years, a zoo of state-of-the-arttechniques aiming to address these safety concerns has emerged. This workprovides a structured and broad overview of them. We first identify categoriesof insufficiencies to then describe research activities aiming at theirdetection, quantification, or mitigation. Our paper addresses both machinelearning experts and safety engineers: The former ones might profit from thebroad range of machine learning topics covered and discussions on limitationsof recent methods. The latter ones might gain insights into the specifics ofmodern ML methods. We moreover hope that our contribution fuels discussions ondesiderata for ML systems and strategies on how to propel existing approachesaccordingly.

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety